AI Agent Engineer
Blockchain intelligence company providing tools to detect, investigate, and manage crypto-related fraud, financial crime, and compliance for institutions and government agencies.
Funding history
Projects
About TRM Labs
TRM Labs provides blockchain intelligence for investigations and compliance, offering products such as forensics, wallet screening, entity screening, transaction monitoring, and APIs. It serves financial institutions, crypto businesses, and public sector agencies to trace funds, assess risk, and build cases across digital assets.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will architect and implement robust agentic frameworks that support tool use, context retrieval, memory, and planning. You will build modular agents to automate investigative tasks and augment analyst decision-making. You will extend and scale LLM infrastructure, including prompt engineering, retrieval augmented generation, model serving, and evaluation loops. You will design safe, observable, and auditable agent behaviors and evaluate performance across metrics like reasoning, latency, success rate, and hallucination, iterating based on user feedback and telemetry.
Requirements
- Strong engineering background with deep experience in backend or systems work (Python preferred)
- Hands-on experience building with LLMs, agents, and tooling frameworks such as LangChain, semantic caches, and vector databases
- Comfort working with agentic pipelines and optimizing information flow into AI systems
- Thoughtful approach to system design with emphasis on safety, scalability, and explainability
- High product empathy and a bias toward experimentation and iteration
- Experience with knowledge graphs, task orchestration, or AI safety a plus
Responsibilities
- Architect and implement a robust agentic framework that supports tool use, context retrieval, memory, and planning
- Build intelligent, modular agents that automate investigative tasks and augment analyst decision-making
- Extend and scale LLM infrastructure including prompt engineering, RAG, and evaluation loops
- Design safe, observable, and auditable agent behaviors ensuring reliability in high-sensitivity environments
- Evaluate performance across metrics like reasoning, latency, success rate, and hallucination and iterate based on feedback and telemetry
- Contribute to rapid experimentation and ethical AI deployment
Benefits
- Equity plan eligibility
